{"id":"W1983804081","doi":"10.1109/jstars.2015.2400134","title":"Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Beijing Normal University; University of Windsor","keywords":"Photosynthetically active radiation; Red edge; Vegetation (pathology); Remote sensing; Chlorophyll; Normalized Difference Vegetation Index; Environmental science; Canopy; Leaf area index; Mathematics; Botany; Hyperspectral imaging; Photosynthesis; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00153793,0.0001172225,0.0003095866,0.0001705812,0.00005594662,0.00001551741,0.00009452007,0.0001596271,0.000002629901],"category_scores_gemma":[0.0008638891,0.0001047178,0.00003312346,0.0007598459,0.00009904017,0.0003131107,0.00002495405,0.0001821229,1.715904e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001648841,"about_ca_system_score_gemma":0.0001631238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002143232,"about_ca_topic_score_gemma":0.00006160564,"domain_scores_codex":[0.9979389,0.0001406596,0.0008547944,0.0001750475,0.0007738561,0.0001167547],"domain_scores_gemma":[0.9972625,0.0001498154,0.00143047,0.0001803808,0.000930813,0.00004604448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007689671,0.00004036444,0.0001759215,0.0000295606,0.00004468676,2.900487e-7,0.0006313727,0.5765295,0.3216511,0.000005187796,0.000003687241,0.1008114],"study_design_scores_gemma":[0.001110503,0.00005769005,0.04365231,0.0001513587,0.000208605,0.00001256933,0.0001380273,0.8638926,0.08922343,0.001459361,0.00001049081,0.00008302407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703552,0.0000353233,0.02889944,0.00004788479,0.0001233152,0.0004226113,0.000005631859,0.000005684503,0.0001049374],"genre_scores_gemma":[0.923964,0.00002028337,0.07592005,0.000006556636,0.00004105955,2.029084e-8,0.00003619902,0.00001005171,0.000001804024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2873631,"threshold_uncertainty_score":0.4270269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05948470629532158,"score_gpt":0.2968411536824906,"score_spread":0.237356447387169,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}